Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pore network modeling is a widely used technique for simulating multiphase transport in porous materials, but there are very few software options available. This work outlines the OpenPNM package that was jointly developed by several porous media research groups to help address this gap. OpenPNM is written in Python using NumPy and SciPy for most mathematical operations, thus combining Python's ease of use with the performance necessary to perform large simulations. The package assists the user with managing and interacting with all the topological, geometrical, and thermophysical data. It also includes a suite of commonly used algorithms for simulating percolation and performing transport calculations on pore networks. Most importantly, it was designed to be highly flexible to suit any application and be easily customized to include user-specified pore-scale physics models. The framework is fast, powerful, and concise. An illustrative example is included that determines the effective diffusivity through a partially water-saturated porous material with just 29 lines of code.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it